The potential function network uses a UNet-based encoder-decoder architecture [1]}. This model is trained on a dataset of semantic maps as described in sec:offlinelearning. For Gibson, we extract 63 train / 13 val maps from each floor in Gibson tiny. For MP3D, we extract 153 train / 21 val maps from each floor. For each dataset, we pre-compute \(400@000\)  train / \(1@000\)  val \((m^p, U^a, U^o)\)  tuples as discussed in sec:offlinelearning. We train the model using PyTorch [2]} for 3 epochs. We use the Adam optimizer [3]} with a learning rate of \(0.001\)  that is decayed by a factor of 10 after 2 epochs.
